mmg_233_2013_genetics_genomicswikiaorg-20200214-history
Functional Metagenomics of the Human Gut and Prebiotic Breakdown Pathways
Metagenomics is the genomic analysis of a collection of organisms. This field developed in order to study previously uncultured microorganisms. These uncultured organisms represent the vast majority of organisms living in most environments on earth (1). Functional metagenomic studies uses high resolutions genomic analysis in order to connect microorganism to particular functions within an environment (2). The human gut is host to numerous bacteria the types of which primarily belong to the phylums of Actinobacteria, Bacteroidetes, and Firmicutes (3). These bacteria have many metabolic enzymes that are vital to maintaining the health of the host (4). Of particular importance are enzymes that are involved in the transport and metabolism of dietary carbohydrates (5). Dietary carbohydrates are metabolized by gut bacteria to produce metabolic intermediates such as lactate, pyruvate, and short chain fatty acids that are useable by the host organism and contribute to its well being (6). Because of the benefits of gut bacteria, interest in compounds that stimulate their growth, known as prebiotics, has increased (7). Most known prebiotics are dietary carbohydrates. However, there is little information about what enzymes are involved in the metabolism prebiotics by gut flora (8). It was the goal of Cecchini et. al. to use a functional metagenomic approach to identify genes in gut bacteria that encode enzymes which are responsible for metabolizing prebiotics. Functional Metagenomic Analysis Initially a metagenomic library was created by taking two separate gut microbiota samples. A fecal sample was taken from a healthy 30 year old male and a second sample was taken from the distal ileum of a 50 year old man undergo a colonoscopy. Metagnomic DNA was then extracted and 30 to 40kb fragments were cloned into fosmids. Fosmids can be packaged with up 40kb segments of DNA. 20,000 clones of each library, designated I for the ileum sample and F for the fecal sample, were created and covered a total of 1.4gb of metagenom ic DNA. In order to determine what prebiotics these constructs could potentially metabolize, each clone was grown in the presences of a carbohydrate that would serve as the only source of carbon. Clones that were capable of growing in those conditions were deemed hits. The hits varied from 0.2% to 1.35% of clones tested depending on the carbohydrate and what library the clone was from (Figure 2). High Performance Anion Exchange Chromatography with Pulsed Amperometric Detection (HPAEC-PAD) was performed on the hit clones in order to determine their ability to metabolize specific carbohydrates. This was done by incubating the cellular extracts of individual clones with a particular carbohydrate and then analyzing the reaction via HPAEC-PAD. HPAEC-PAD was used to determine the carbohydrate profile of each reaction and thus can provide information on how well a clone metabolizes a specific carbohydrate. Depending on how the clones were able to metabolize specific c arbohydrates they were organized into 19 categories (Figure 3). 17 clones were selected for pyrosequencing. These clones were representative of clusters that contained the most efficient clones. Each clone generated one contig, except for clones 9 and 10 which generated two contigs. Analysis of the contigs revealed 172 open reading frames (ORF) for the F library and 158 ORFs for the I library. 26% of all ORFs encoded for proteins were involved in carbohydrate transport and metabolism. 38 of the ORFs were annotated as genes encoding carbohydrate activate enzymes (CAZymes) which are enzymes involved in modifying carbohydrates. 33 of the CAZymes were predicted to be glycoside-hydrolyses (GH), with the encoding either esterases or glycosyl-transferases. However esterases and glycosyl-transferases are not involved in prebiotic metabolism. Further analysis revealed that the GHs were or ganized into operon like multi-gene clusters (Figure 4). These clusters encoded proteins that were involved in carbohydrate binding, transport, and metabolism. In order to determine the contribution of specific types of bacteria to prebiotic metabolism the researchers attempted to determine the origin of the metagenomic DNA. This was done by comparing the sequences of the contigs to reference sequences. None of the sequences matched up perfectly indicating that the bacteria the contigs came from had not been sequenced. However due to the high similarity of several of the contigs (F2, F4, F6, I10, I11, I13, and I14) to the reference sequences the species of bacteria the contig came from was able to be identified. Based on this analysis Bifidobacterium longum, Biofidobacterium adolescentis, Eubacterium rectale, and Steptococcus therophilus were all identified with high probability (Figure 5). Use of the metagenome analyzer (MEGAN) with low stringent criteria allowed for the further identification of the bacterial orders Clostridiales and Bacteroidales. Clone I9, which generated two contigs which were impossible to assemble, was subject to further analysis. This was done by using PCR to amplify one gene in each contig. This verified that that the contigs did in fact come from one metagenomic insert. One of the contigs was believed to come from Dorea longicatena and the other from Clostridium nexile. The researchers beleived that this could be due to horizontal gene transfer and could be an example of the of plasticity of the gut metagenome. Furthermore the fecal and distal ileum metagenomic samples were also compared to 3 different libraries of randomly sequenced fecal metagenomes. It was found that 87% of the genes identified in the study were at least 90% identical to genes identified in the library samples. Overall the researchers believed that they were able to identify enzymes involved in the metabolism of prebiotics as well as some of the microorganisms that are responsible. They also demonstrated that there are a still unknown bacteria in the human gut. Furthermore the researchers have shown that the effects of prebiotics differ based on gut location. Sources 1. Handelsman J. Uncultured Microorganisms. Microbiology and Molecular Biology Reviews 2004; 68(4): 669-685. 2.Chistoserdovai L. Functional metagenomics: recent advances and future challenges. Biotechnol Genet Eng Rev 2010; 26: 335-353. 3. Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L. Diversity of the human intestinal microbial flora. Science 2005; 308: 1635-1638. 4. Ottman N, Smidt H, deVos WM, Belzer C. The function of our microbiota: who is out there and what do they do? Front Cell Infect Microbiol 2012; 2: DOI 10.3389/fcimb.2012.00104. 5. Gill SR, Pop M, Deboy RT, Eckburg PB, Turnbaugh PJ. Metagenomic analysis of the human gut microbiome. Science 2006; 312: 1355-1359. 6. Cummings JH, Macfarlane GT. The control and consequences of bacterial fermentation in the human colon. J Appl Bacteriol 1991; 70: 443-459. 7. Figueroa-Gonzalez I, Cruz-Guerro A, Quijano G. The benefits of probiotics on human health. J Microbial Biochem Technol 2011; S1: 003. 8. Cecchini DA, Laville E, Laguerre S, Robe P, Leclerc M, Dore J, Henrissat B, Remaud-Simeon M, Monsan P, Potocki-Veronese G. Functional metagenomics reveals novel pathways of prebiotic breakdown by human gut bacteria. PLoS 2013; 8(9): e72766. 9. Sartor RB. Microbial influences in inflammatory bowel diseases. Gastroenterology 2008; 134: 577-594.